Context Embedding Networks

Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Existing models for learning embeddings from the crowd typically make simplifying assumptions such as all individuals estimate similarity using the same criteria, the list of criteria is known in advance, or that the crowd workers are not influenced by the data that they see. To overcome these limitations we introduce Context Embedding Networks (CENs). In addition to learning interpretable embeddings from images, CENs also model worker biases for different attributes along with the visual context i.e. the visual attributes highlighted by a set of images. Experiments on two noisy crowd annotated datasets show that modeling both worker bias and visual context results in more interpretable embeddings compared to existing approaches.Comment: CVPR 2018 spotligh

Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings

Multi-hazard performance of bridge timber piles retrofitted with fiber reinforced polymer composites

Bridges with various timber structural components make up a large portion of the transportation infrastructure in the US. Bridges supported on timber pile substructure, simply referred to as timber pile bridges, are particularly common. Many timber pile bridges still in service today were constructed in the 1950’s and 60’s using simplified design approaches largely based on convention and empirical data. Since only gravity loads were considered in their original design, many timber pile bridges are deficient by modern standards. Further exacerbating this problem is their age and the susceptibility to degradation. Despite these issues, timber bridges in general are overlooked in terms of operational importance and afforded minimal maintenance effort. Furthermore, whereas countless research studies have focused on every aspect of conventional reinforced concrete or steel bridges, research activity on timber bridges has been almost non-existent. Given ever increasing demands on bridges and interest in sustainable, resilient structures, the time is now to close the gap. The first part of this research is devoted to the experimental testing of timber piles with a special focus on the short- and long-term performance of piles retrofitted with fiber reinforced polymer (FRP) composites. The impact of timber deterioration and the effectiveness of different FRP application strategies are examined to make retrofit design recommendations, and a unique accelerated aging procedure is used to study their durability. In the second part of the research, numerical approaches are used to develop methods for estimating the capacity of deteriorated timber pile bridge substructure. This includes a comprehensive load rating method for abutment timber piles in which the in-situ pile condition is a key input parameter as well as performance prediction models for bridges subject to earthquake-tsunami hazards. Under earthquake-tsunami loading considerations, a sequential analysis framework is used to perform nonlinear dynamic time history analyses and tsunami impact simulations using the particle finite element method (PFEM). Failure criteria for a typical bridge are introduced in the form of earthquake-tsunami hazard interaction diagrams then a probabilistic approach is used to quantify the effect of damage accumulation and introduce the concept of a demand amplification factor. The findings from this research clearly demonstrate the need to more carefully consider the safety of existing timber bridges, and show that proper maintenance and retrofitting can significantly improve their strength and durability. Most importantly, this research contributes simple and robust tools for assessing the vulnerability of timber pile bridges under both typical service conditions and multi-hazard scenarios

A screening method for mild cognitive impairment in elderly individuals combining bioimpedance and MMSE

We investigated a screening method for mild cognitive impairment (MCI) that combined bioimpedance features and the Korean Mini-Mental State Examination (K-MMSE) score. Data were collected from 539 subjects aged 60 years or older at the Gwangju Alzheimer’s & Related Dementias (GARD) Cohort Research Center, A total of 470 participants were used for the analysis, including 318 normal controls and 152 MCI participants. We measured bioimpedance, K-MMSE, and the Seoul Neuropsychological Screening Battery (SNSB-II). We developed a multiple linear regression model to predict MCI by combining bioimpedance variables and K-MMSE total score and compared the model’s accuracy with SNSB-II domain scores by the area under the receiver operating characteristic curve (AUROC). We additionally compared the model performance with several machine learning models such as extreme gradient boosting, random forest, support vector machine, and elastic net. To test the model performances, the dataset was divided into a training set (70%) and a test set (30%). The AUROC values of SNSB-II scores were 0.803 in both sexes, 0.840 for males, and 0.770 for females. In the combined model, the AUROC values were 0.790 (0.773) for males (and females), which were significantly higher than those from the model including MMSE scores alone (0.723 for males and 0.622 for females) or bioimpedance variables alone (0.640 for males and 0.615 for females). Furthermore, the accuracies of the combined model were comparable to those of machine learning models. The bioimpedance-MMSE combined model effectively distinguished the MCI participants and suggests a technique for rapid and improved screening of the elderly population at risk of cognitive impairment

Simultaneous Inference of a Partially Linear Model in Time Series

We introduce a new methodology to conduct simultaneous inference of the nonparametric component in partially linear time series regression models where the nonparametric part is a multivariate unknown function. In particular, we construct a simultaneous confidence region (SCR) for the multivariate function by extending the high-dimensional Gaussian approximation to dependent processes with continuous index sets. Our results allow for a more general dependence structure compared to previous works and are widely applicable to a variety of linear and nonlinear autoregressive processes. We demonstrate the validity of our proposed methodology by examining the finite-sample performance in the simulation study. Finally, an application in time series, the forward premium regression, is presented, where we construct the SCR for the foreign exchange risk premium from the exchange rate and macroeconomic data.Comment: 61 pages, 6 figure

Inference and Forecasting Based on the Phillips Curve

In this paper, we conduct uniform inference of two widely used versions of the Phillips curve, specifically the random-walk Phillips curve and the New-Keynesian Phillips curve (NKPC). For both specifications, we propose a potentially time-varying natural unemployment (NAIRU) to address the uncertainty surrounding the inflation-unemployment trade-off. The inference is conducted through the construction of what is known as the uniform confidence band (UCB). The proposed methodology is then applied to point-ahead inflation forecasting for the Korean economy. This paper finds that the forecasts can benefit from conducting UCB-based inference and that the inference results have important policy implications

A New Test for Market Efficiency and Uncovered Interest Parity

We suggest a new single-equation test for Uncovered Interest Parity (UIP) based on a dynamic regression approach. The method provides consistent and asymptotically efficient parameter estimates, and is not dependent on assumptions of strict exogeneity. This new approach is asymptotically more efficient than the common approach of using OLS with HAC robust standard errors in the static forward premium regression. The coefficient estimates when spot return changes are regressed on the forward premium are all positive and remarkably stable across currencies. These estimates are considerably larger than those of previous studies, which frequently find negative coefficients. The method also has the advantage of showing dynamic effects of risk premia, or other events that may lead to rejection of UIP or the efficient markets hypothesis

Radar-based nowcasting by combining centroid tracking and motion vector of convective storm

Póster presentado en: 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Effects of 12 Weeks Weight Training and Plyometric Training on Body Composition, Physical Fitness and Electronic Hogu Hitting Ability in Taekwondo Sparring Athletes

OBJECTIVES The purpose of this study is to identify the effects of differences in muscle function training of Taekwondo sparring athletes on body composition, basic physical fitness, isokinetic muscle function, and electronic hogu hitting ability, and to present basic data for a training program for Taekwondo sparring athletes. METHODS This study randomly sampled 25(M: 20, F: 5) Taekwondo sparring athletes. The sampled subjects were divided into a weight training group (n=8), a plyometric training(plyometric) group (n=8), and a control group (n=9) and trained for 60 minutes, 5 times a week, for 12 weeks. Body composition, basic physical fitness, isokinetic muscle function, and electronic hogu hitting ability were evaluated before and after training. Statistical tests of RM Two-way ANOVA were conducted to verify the interaction between groups and times, main effects of times, and main effects between groups according to 12 weeks of training. Post-hoc was conducted using paired-T test(times) and One-way ANOVA test(groups). RESULTS Taekwondo sparring athletes showed positive changes in body composition(weight, BMI, Lean body mass, % body fat, WHR), basic physical fitness(muscle endurance, flexibility), isokinetic muscle function(knee endurance, low back strength), and electronic hogu hitting ability(round house kick, Turning back kick, number of hit) after participating in weight training for 12 weeks (All p<.05). Additionally, positive changes were observed in flexibility and electronic hogu hitting ability(Turning back kick) after participating in plyometric training for 12 weeks (All p<.05). CONCLUSIONS Weight training for 12 weeks in Taekwondo sparring athletes results in positive changes in body composition, increased flexibility and muscular endurance, increases in knee isokinetic muscular endurance and low back isokinetic strength, and improvement in overall electronic hogu hitting ability. Plyometrics for 12 weeks result in increased flexibility and increased electronic hogu hitting ability for back kick. Weight training shows greater improvement in strength and kick endurance than plyometrics